Ronald Domi

Ronald Domi

MSc AI & Bioinformatics · University of Zurich

ronald.domi@uzh.ch
Background

About me

Education

M.Sc. AI/ML & Bioinformatics — UZH, Zurich Graduating 2027

B.Sc. Computer Science — UGA, Grenoble Graduated 2023

Experience

Full Stack Developer ETHZ FGCZ, Zurich · 09/2024 – Present
Front-End Software Developer Ontopic, Bolzano, Italy · 05/2021 – 12/2021
Front-End Software Developer Tirana, Albania · 04/2018 – 08/2019

I am a Master's student in Artificial Intelligence at the University of Zurich, with a minor in Bioinformatics. I work at ETHZ/FGCZ as a Full Stack Developer, building and maintaining software tooling around scientific data workflows.

During my time at FGCZ, I co-authored a publication in BMC Bioinformatics on GEO Uploader, a tool for automating scientific data submission workflows. That project required understanding a real research process well enough to build something domain experts could actually use.

Prior to FGCZ, I worked at Ontopic in Bolzano, working on an Angular codebase with Agile methodologies. Before that, I worked as a Front-End Software Developer in Tirana, implementing UI from design specs and building client-facing software. I started programming in high school with Python and quickly moved into web development, taking on freelance projects while still in school.

My Bachelor's was in Computer Science at Université Grenoble Alpes, where I ranked first in my final year. The degree covered compilers, interpreters, databases, automata, and formal logic. I pick up new technology quickly — I have done it with languages, frameworks, and entire domains.

Languages
English Professional French Professional German Conversational Italian Conversational Albanian Native
Interests
Guitar Tennis Gym Taekwondo Philosophy
Research

Publications

BMC Bioinformatics · Springer Nature · 2026
GEO uploader: simplifying the data deposition in the GEO repository
Ronald Domi, Falko Noé, Peter Leary, Hubert Rehrauer — BMC Bioinformatics 27:140 (2026)

The Gene Expression Omnibus (GEO) repository requires complex multistep submissions involving metadata preparation, FTP uploads, and MD5 validation. Current manual processes are error-prone, time-consuming, and require significant bioinformatics expertise, creating barriers for many researchers. We present GEO Uploader, a web-based tool that automates the entire GEO submission workflow through an intuitive interface. The application reduces the submission initiation time from 2–3 h to under 20 s by automating file uploads, MD5 calculations, and metadata template population.

Work

Projects

A collection of software I have built — across bioinformatics, machine learning, and web development.

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